The management of inventory and queueing systems lies in the heart of operations research and plays a vital role in many business enterprises. To this date, the majority of work in the literature has been done under complete distributional information about the uncertainties inherent in the system. However, in practice, the decision maker may not know the exact distributions of these uncertainties (such as demand, capacity, lead time) at the beginning of the planning horizon, but can only rely on realized observations collected over time. This thesis focuses on the interplay between learning and optimization of three canonical inventory and queueing systems and proposes a series of first online learning algorithms.
The first system studied in Chapter II is the periodic-review multiproduct inventory system with a warehouse-capacity constraint. The second system studied in Chapter III is the periodic-review inventory system with random capacities. The third system studied in Chapter IV is the continuous-review make-to-stock M/G/1 queueing system. We take a nonparametric approach that directly works with data and needs not to specify any (parametric) form of the uncertainties. The proposed online learning algorithms are stochastic gradient descent type, leveraging the (sometimes non-obvious) convexity properties in the objective functions. The performance measure used is the notion of cumulative regret or simply regret, which is defined as the cost difference between the proposed learning algorithm and the clairvoyant optimal algorithm (had all the distributional information about uncertainties been given). Our main theoretical results are to establish the square-root regret rate for each proposed algorithm, which is known to be tight. Our numerical results also confirm the efficacy of the proposed learning algorithms.
The major challenges in designing effective learning algorithms for such systems and analyzing them are as follows. First, in most retail settings, customers typically walk away in the face of stock-out, and therefore the system is unable to keep track of these lost-sales. Thus, the observable demand data is, in fact, the sales data, which is also known as the censored demand data. Second, the inventory decisions may impact the cost function over extended periods, due to complex state transitions in the underlying stochastic inventory system. Third, the stochastic inventory system has hard physical constraints, e.g., positive inventory carry-over, warehouse capacity constraint, ordering/production capacity constraint, and these constraints limit the search space in a dynamic way.
We believe this line of research is well aligned with the important opportunity that now exists to advance data-driven algorithmic decision-making under uncertainty. Moreover, it adds an important dimension to the general theory of online learning and reinforcement learning, since firms often face a realistic stochastic supply chain system where system dynamics are complex, constraints are abundant, and information about…
Advisors/Committee Members: Duenyas, Izak (committee member), Shi, Cong (committee member), Jasin, Stefanus (committee member), Nagarajan, Viswanath (committee member).

The United States cheese consumption has grown considerably over the years. Using Nielsen Homescan panel data for calendar years 2005 and 2006, this dissertation examines the effect of economic and socio-demographic factors on the demand for disaggregated cheese varieties and on the cheese industry in general. In the first essay, we estimated the censored demand for 14 cheese varieties and identified the respective own-price and cross-price elasticities. Also, non-price factors were determined affecting the purchase of each variety as well as the impact of generic dairy advertising. Results revealed that most of the natural cheese varieties have an elastic demand while the processed cheese products exhibited inelastic demands. Strong substitution and complementarity relationships were identified as well, and a two quarter carry-over effect of advertising was observed for most of cheese demands. Results also showed that household demographics affected the demands differently, depending on the nature of the cheese varieties.
The second essay examined the impact of retail promotion on the decision to purchase private label processed cheese products using a probit model. A strong negative relationship was found between national brand manufacturer couponing activity and the private label purchase decision. Therefore, national brand couponing appears to be an effective strategy for manufacturers to deter private label growth. This analysis also shows that the decision of purchasing a private label cheese product is influenced by socio-demographic characteristics of the household, namely household income and size, age and education level of the household head, race, ethnicity, and location.
In the third study, the feasibility of fortifying processed cheese with omega-3 is investigated. This ex-ante analysis took into account the market conditions and evaluates the increase in the demand for processed cheese needed to offset the costs of fortification in order to maintain the profitability of manufacturers like Kraft. Initially, the censored demand for processed cheese products is estimated using panel data; subsequently, the profitability of manufacturing such product is determined.This analysis shows that, within reasonable market conditions and reasonable marginal costs, the fortification of processed cheese products with omega-3 fatty acids indeed is feasible from a profitability standpoint to manufacturers.
Advisors/Committee Members: Capps, Oral (advisor), Bessler, David A. (committee member), Wu, Ximing (committee member), Ureta, Manuelita (committee member).

Bouhlal, Y. (2012). A Retrospective and Prospective Analysis of the Demand for Cheese Varieties in the United States. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10745

Note: this citation may be lacking information needed for this citation format:Not specified: Masters Thesis or Doctoral Dissertation

Note: this citation may be lacking information needed for this citation format:Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bouhlal, Yasser. “A Retrospective and Prospective Analysis of the Demand for Cheese Varieties in the United States.” 2012. Web. 07 Jun 2020.

Vancouver:

Bouhlal Y. A Retrospective and Prospective Analysis of the Demand for Cheese Varieties in the United States. [Internet] [Thesis]. Texas A&M University; 2012. [cited 2020 Jun 07].
Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10745.

Note: this citation may be lacking information needed for this citation format:Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bouhlal Y. A Retrospective and Prospective Analysis of the Demand for Cheese Varieties in the United States. [Thesis]. Texas A&M University; 2012. Available from: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10745

Note: this citation may be lacking information needed for this citation format:Not specified: Masters Thesis or Doctoral Dissertation